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Plants play a crucial role in ecosystems as primary producers and are essential for the well-being of humanity, fulfilling diverse needs including sustenance, medicine, and the provision of raw materials for various industries. The project delineates a notable initiative aimed at addressing the persistent issue of plant diseases among smallholder farmers through advanced technology. Utilizing the widespread prevalence of smartphones and advancements in computer vision, the research centers on evaluating the effectiveness of Convolutional Neural Networks (CNNs), specifically focusing on the ResNet34 model, MobileNetV2 in swiftly and accurately detecting crop diseases. The CNN model, crafted and fine-tuned with a dataset encompassing 8,685 leaf images acquired under controlled conditions, attains commendable validation outcomes, boasting an accuracy rate of 97.2% and an F1 score surpassing 96.5%. The implementation of this model as a web application facilitates accessibility, empowering farmers to employ the technology for discerning seven distinct plant diseases amid healthy leaf tissue. This study underscores the practicality of incorporating CNNs in agricultural settings, representing a substantial advancement towards AI-driven solutions poised to fortify the resilience and productivity of smallholder farmers.

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Progressing Plant Leaf Disease Detection with CNN: Addressing Environmental Obstacles via Adaptive Normalization and Active Learning Strategies

  • 会议论文

  • 发表日期：

    2026年

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  • S. Anuradha,
  • Nagella NagaPrasanna,
  • Mitikiri Bhavani,
  • Ravilla Sowmya,
  • SajjanaGandla Harika,
  • T. Brahmananda Reddy

摘要

Plants play a crucial role in ecosystems as primary producers and are essential for the well-being of humanity, fulfilling diverse needs including sustenance, medicine, and the provision of raw materials for various industries. The project delineates a notable initiative aimed at addressing the persistent issue of plant diseases among smallholder farmers through advanced technology. Utilizing the widespread prevalence of smartphones and advancements in computer vision, the research centers on evaluating the effectiveness of Convolutional Neural Networks (CNNs), specifically focusing on the ResNet34 model, MobileNetV2 in swiftly and accurately detecting crop diseases. The CNN model, crafted and fine-tuned with a dataset encompassing 8,685 leaf images acquired under controlled conditions, attains commendable validation outcomes, boasting an accuracy rate of 97.2% and an F1 score surpassing 96.5%. The implementation of this model as a web application facilitates accessibility, empowering farmers to employ the technology for discerning seven distinct plant diseases amid healthy leaf tissue. This study underscores the practicality of incorporating CNNs in agricultural settings, representing a substantial advancement towards AI-driven solutions poised to fortify the resilience and productivity of smallholder farmers.